基于AHP-PSR模型的西南丘陵地区农用地集约利用动态评价

针对西南丘陵地区农用地利用效率低、耕地质量锐减、人地关系逐步恶化等问题构建了基于AHP-PSR模型的西南丘陵地区农用地集约利用评价系统。采用专家打分法进行指标筛选后,基于AHP-PSR模型进行农用地集约利用动态评价,对系统内部协调及稳定性进行分析、评估,较深入地研究农用地集约利用驱动因子,并采用最优子集选取策略分析集约利用驱动力,以避免仅以简单相关性就加入或剔除驱动力因素导致回归系数不符合实际内涵的现象。实验表明:可利用系统调整指标间权重,动态展示人地关系协调可持续发展关系,其分析结果可供有关部门决策参考。     

利用气象要素计算五道梁地区土壤热流量的试验

根据五道梁地区1994—1997年的热流板测量土壤热流值,初步分析了该地区土壤热流随时间的变化特征。利用1994—1996年的月平均资料分别分析了总辐射、地表温度、地表温度和80 cm深度温度差和2 cm、10 cm深度的土壤热流的相关性,并建立了相应的回归方程;利用得到的回归方程对1997年的土壤热流作计算检验,就月平均资料来说,用总辐射和地温差来计算浅层土壤热流结果是比较可靠的。     

珠江流域夏半年降水的预测

采用珠江流域1954～2003年月降水资料,以及前期太平洋海温场和北半球500 hPa高度场月平均资料,利用主分量逐步回归预测模型,对该流域夏半年(4～9月)降水分布进行主分量计算和预测.结果表明:对前25个主分量的预测就能很好地预测珠江流域夏半年降水的变化,2004～2006年预测结果与实况较接近.预测因子取自500 hPa高度场和海温场的主分量,为降水的预测提供大量有用信息.该方法在实际业务中有一定的应用价值.     

Allocycles and Autocycles in the Upper Part of the Limestone Coal Group (Pendleian E1) in the Glasgow–Stirling Region of the Midland Valley of Scotland

The upper part of the Limestone Coal Group (Pendleian E₁), between the major marine transgressions of the Black Metals and the Index Limestone, represents an early example of ‘coal measures’ facies. It comprises a distal and a proximal facies association. The distal facies association, which was subject to relatively strong marine influences and included both deltaic and fluvial elements, is characterized by a regular ‘layer-cake’ succession with laterally-persistent lithological members. By contrast, the proximal association, which was more subject to fluvial influences and is typified by the variable Bannockburn Main Complex, is characterized by abrupt lateral changes in lithofacies. The distal facies association is dominant in the west of the Kilsyth Trough, but proximal facies intercalations increase eastwards, so that they constitute a fairly high proportion of the succession in the Kincardine Basin, particularly in areas of locally-increased tectonic subsidence that were frequently occupied by major channel belts. Some of the sheet sandstones within the distal association have a lower, upward-coarsening portion succeeded by a coarser-grained, erosive-based, upward-fining portion. The resulting ‘two-storey’ profile may reflect deltaic sand bodies having been suceeded by fluvial sand bodies, following a general fall in base level. Linear regression lines showing the relationship between the number of horizons colonized by vegetation and net subsidence, suggest that local autocyclic, tectonosedimentary processes, such as delta switching, channel migration, and avulsion, were superimposed upon a widespread allocyclic, probably glacial-eustatic, process. The former processes were most effective within the proximal facies association and in the Kincardine Basin and the latter in the distal association and the Kilsyth Trough.     

广西土壤有机质空间变异特征及其影响因素研究

基于广西第二次土壤普查的270个土壤剖面资料,结合1∶50万数字化土壤类型图、土地利用类型图和气象监测数据等资料,利用地统计学和逐步回归分析等方法对广西表层土壤有机质空间变异特征及其影响因素进行了探究。结果表明：广西表层土壤有机质平均含量为3.11±2.19%,变异系数为70.72%,空间分布呈北高南低的趋势。广西表层土壤有机质空间分布受到自然和人为因素的共同影响,土壤类型、成土母质、海拔、土地利用、气候和坡度6个环境因子对全区土壤有机质含量变异的综合解释能力为47.9%。其中,土壤类型是最重要的影响因素,能独立解释其变异的36.0%,海拔和成土母质分别能独立解释28.5%和15.8%。气温对广西土壤有机质空间分布的影响比降水量更加显著,从而造成了广西土壤有机质整体呈南低北高的趋势。同时,土壤有机质对气温的敏感性在一定程度上受到降雨量的制约。此外,研究区农业耕作管理等因素对土壤有机质的影响也不容忽视。     

基于角质颚长度的头足类种类判别

本文根据2013 年8 月于舟山市沈家门东河菜场采集的5 种近海常见经济头足类(剑尖枪乌 贼、杜氏枪乌贼、金乌贼、曼氏无针乌贼和短蛸), 利用逐步判别和主成分分析法对其上、下角质颚 的各5 种长度指标(喙长、头盖长、脊突长、侧壁长、翼长)进行分析。结果显示, 这5 种头足类角质 颚长度差异显著(ANOVA: P<0.001), 其中以金乌贼的角质颚尺寸最大, 短蛸的角质颚尺寸最小。判 别分析显示, 角质颚长度适合用来划分头足类的种类, 综合判别成功率为96.2%, 其中以上颚侧壁 长、上颚头盖长和下颚脊突长对判定的贡献率最高。然而, 与之相比, 标准化后的角质颚长度则更适 合用来划分头足类的种类, 综合判别成功率达到100%, 其中以标准化下颚喙长和标准化下颚翼长对 判定的贡献率最高。主成分分析显示, 角质颚长度及标准化后角质颚长度对枪乌贼类和乌贼类的判 定成功率均达到100%.本文建立了一种基于角质颚长度判别分析法的头足类种类判定的新方法, 丰 富了头足类种类鉴定的技术手段, 为我国学者在相关领域的研究提供了基础。     

泉州湾赤潮藻类优势种细胞密度回归方程研究

于2006年5-11月对东海泉州湾赤潮监控区四个监测站位开展赤潮常规监测。根据监测结果,分别以各站位23项水质理化生物环境因子指标为自变量,相应赤潮藻类优势种的细胞密度为因变量,进行多元逐步回归分析,建立了各站位优势种中肋骨条藻(Skeletonema costatum)、太平洋海链藻(Thalassiosira pacifica)、微小原甲藻(Prorocentrum minimum)、尖刺拟菱形藻(Pseudo-nitzschia pungens)、丹麦细柱藻(Leptocylindrus danicus)和旋链角毛藻(Chaetoceros curvisetus)等的细胞密度多参量回归方程。结果表明,所有回归方程的复相关系数都接近于1,方差分析的结果均为回归极显著,表明所建立的回归方程可作为相应赤潮优势种细胞密度预报方程的高度有效性,将对今后泉州湾的赤潮预报提供良好的指导作用。     

改进的DisTrad模型在地形起伏区地表温度空间分辨率提升的应用

DisTrad（Disaggregation procedure for radiometric surface temperature）模型是常用于遥感地表温度空间分辨率提升的主要模型之一。DisTrad模型常面向空间范围有限、地形相对平坦的研究区域,且常选用植被参数（如植被指数或植被覆盖度等）作为关键参数。然而在空间范围较大、地形起伏地区,地表温度的空间变异可能无法完全通过植被参数解释。本研究选取四川盆地及毗邻地区为研究区,通过模拟数据研究DisTrad模型在地形起伏区地表温度空间分辨率提升中的适用性。数字高程模型（Digital Elevation Model,DEM）、归一化差值植被指数（Normalized Difference Vegetation Index,NDVI）等参数,采用滑动窗口逐步回归,将空间分辨率为6km的地表温度提升至空间分辨率为1km。研究结果表明,改进的模型在平原及海拔较低的高原地区提升获得的地表温度空间分辨率具有较高精度,均方根误差（Root Mean Square Error,RMSE）为0.5K左右;在地形起伏较大的地区,RMSE为4K,验证了改进的模型提升的可行性。     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

使用Python函数式编程进行空间回归分析

针对使用传统的编程方式进行空间回归分析过于繁杂的问题,本文提出将Python函数式编程引入空间回归分析方法,分析和探讨脚本语言＋函数式编程方式在GIS数据分析中的推广应用前景。编程实践证明,采用Python函数式编程,结合既有的开源软件包,可以有效提高编程效率,减少代码量,将实践人员的注意力集中在问题本身。